04. A High Level Review

AI For Trading C6 L1 A02 A High Level Review V3

Introduction to Artificial Intelligence and Machine Learning

Explore the fundamentals of Artificial Intelligence (AI) and delve into machine learning (ML), a crucial AI branch, providing systems with the ability to mimic human thought and problem-solving.

Key Concepts

  • Artificial Intelligence (AI):
    Broad field aiming to replicate human cognitive functions using data science.

  • Machine Learning (ML):

    • Definition: Subset of AI that uses algorithms to learn from large datasets, identifying patterns to make predictions.
    • Types of ML Models:
    • Supervised Learning: Learning with labeled data, e.g., linear regression.
    • Unsupervised Learning: Learning without labeled data, discovering hidden structures.
    • Reinforcement Learning: Learning optimal actions through trial and error.

Linear Regression

  • Simplest form of supervised learning.
  • Relates input (e.g., years of experience) to output (e.g., salary) using a linear function.
  • Involves assuming a function fits the observed data and finding the best approximation with it.

Conclusion

AI and ML revolve around finding relationships in data. Becoming skilled in these areas involves making informed assumptions about data relationships and leveraging ML tools to hone those assumptions.

Which of the following are paradigms of machine learning?
SOLUTION:
  • Unsupervised learning, which identifies patterns within unlabeled data.
  • Supervised learning, where models are trained on labeled data.
  • Reinforcement learning, where agents learn by interacting with an environment.

What is the primary objective when training AI/ML models?
SOLUTION: To make predictions or perform specific tasks by learning from the patterns in available data.

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